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Temperature heavily affects the behavior of any energy storage chemistries. In particular, lithium-ion batteries (LIBs) play a significant role in almost all storage application fields, including Electric Vehicles (EVs). Therefore, a full comprehension of the influence of the temperature on the key cell components and their governing equations is mandatory for the effective integration of LIBs into the application. If the battery is exposed to extreme thermal environments or the desired temperature cannot be maintained, the rates of chemical reactions and/or the mobility of the active species may change drastically. The alteration of properties of LIBs with temperature may create at best a performance problem and at worst a safety problem. Despite the presence of many reports on LIBs in the literature, their industrial realization has still been difficult, as the technologies developed in different labs have not been standardized yet. Thus, the field requires a systematic analysis of the effect of temperature on the critical properties of LIBs. In this paper, we report a comprehensive review of the effect of temperature on the properties of LIBs such as performance, cycle life, and safety. In addition, we focus on the alterations in resistances, energy losses, physicochemical properties, and aging mechanism when the temperature of LIBs are not under control.

In this experimental investigation, we studied the safety and thermal runaway behavior of commercial lithium-ion batteries of type 21700. The different cathode materials NMC, NCA and LFP were compared, as well as high power and high energy cells. After characterization of all relevant components of the batteries to assure comparability, two abuse methods were applied: thermal abuse by the heat-wait-seek test and mechanical abuse by nail penetration, both in an accelerating rate calorimeter. Several critical temperatures and temperature rates, as well as exothermal data, were determined. Furthermore, the grade of destruction, mass loss and, for the thermal abuse scenario, activation energy and enthalpy, were calculated for critical points. It was found that NMC cells reacted first, but NCA cells went into thermal runaway a little earlier than NMC cells. LFP cells reacted, as expected, more slowly and at significantly higher temperatures, making the cell chemistry considerably safer. For mechanical abuse, no thermal runaway was observed for LFP cells, as well as at state of charge (SOC) zero for the other chemistries tested. For thermal abuse, at SOC 0 and SOC 30 for LFP cells and at SOC 0 for the other cell chemistries, no thermal runaway occurred until 350 °C. In this study, the experimental data are provided for further simulation approaches and system safety design.

In recent years, the rapid evolution of transportation electrification has been propelled by the widespread adoption of lithium-ion batteries (LIBs) as the primary energy storage solution. The critical need to ensure the safe and efficient operation of these LIBs has positioned battery management systems (BMS) as pivotal components in this landscape. Among the various BMS functions, state and temperature monitoring emerge as paramount for intelligent LIB management. This review focuses on two key aspects of LIB health management: the accurate prediction of the state of health (SOH) and the estimation of remaining useful life (RUL). Achieving precise SOH predictions not only extends the lifespan of LIBs but also offers invaluable insights for optimizing battery usage. Additionally, accurate RUL estimation is essential for efficient battery management and state estimation, especially as the demand for electric vehicles continues to surge. The review highlights the significance of machine learning (ML) techniques in enhancing LIB state predictions while simultaneously reducing computational complexity. By delving into the current state of research in this field, the review aims to elucidate promising future avenues for leveraging ML in the context of LIBs. Notably, it underscores the increasing necessity for advanced RUL prediction techniques and their role in addressing the challenges associated with the burgeoning demand for electric vehicles. This comprehensive review identifies existing challenges and proposes a structured framework to overcome these obstacles, emphasizing the development of machine-learning applications tailored specifically for rechargeable LIBs. The integration of artificial intelligence (AI) technologies in this endeavor is pivotal, as researchers aspire to expedite advancements in battery performance and overcome present limitations associated with LIBs. In adopting a symmetrical approach, ML harmonizes with battery management, contributing significantly to the sustainable progress of transportation electrification. This study provides a concise overview of the literature, offering insights into the current state, future prospects, and challenges in utilizing ML techniques for lithium-ion battery health monitoring.

The thermal behavior of Li-ion cells is an important safety issue and has to be known under varying thermal conditions. The main objective of this work is to gain a better understanding of the temperature increase within the cell considering different heat sources under specified working conditions. With respect to the governing physical parameters, the major aim is to find out under which thermal conditions a so called Thermal Runaway occurs. Therefore, a mathematical electrochemical-thermal model based on the Newman model has been extended with a simple combustion model from reaction kinetics including various types of heat sources assumed to be based on an Arrhenius law. This model was realized in COMSOL Multiphysics modeling software. First simulations were performed for a cylindrical 18650 cell with a L i C o O 2 -cathode to calculate the temperature increase under two simple electric load profiles and to compute critical system parameters. It has been found that the critical cell temperature T crit , above which a thermal runaway may occur is approximately 400 K , which is near the starting temperature of the decomposition of the Solid-Electrolyte-Interface in the anode at 393 . 15 K . Furthermore, it has been found that a thermal runaway can be described in three main stages.

Lithium-ion batteries experience degradation with each cycle, and while aging-related deterioration cannot be entirely prevented, understanding its underlying mechanisms is crucial to slowing it down. The aging processes in these batteries are complex and influenced by factors such as battery chemistry, electrochemical reactions, and operational conditions. Key stressors including depth of discharge, charge/discharge rates, cycle count, and temperature fluctuations or extreme temperature conditions play a significant role in accelerating degradation, making them central to aging analysis. Battery aging directly impacts power, energy density, and reliability, presenting a substantial challenge to extending battery lifespan across diverse applications. This paper provides a comprehensive review of methods for modeling and analyzing battery aging, focusing on essential indicators for assessing the health status of lithium-ion batteries. It examines the principles of battery lifespan modeling, which are vital for applications such as portable electronics, electric vehicles, and grid energy storage systems. This work aims to advance battery technology and promote sustainable resource use by understanding the variables influencing battery durability. Synthesizing a wide array of studies on battery aging, the review identifies gaps in current methodologies and highlights innovative approaches for accurate remaining useful life (RUL) estimation. It introduces emerging strategies that leverage advanced algorithms to improve predictive model precision, ultimately driving enhancements in battery performance and supporting their integration into various systems, from electric vehicles to renewable energy infrastructures.

Electric vehicles (EVs) and hybrid vehicles (HEVs) are being increasingly utilized for various reasons. The main reasons for their implementation are that they consume less or do not consume fossil fuel (no carbon dioxide pollution) and do not cause sound pollution. However, this technology has some challenges, including complex and troublesome accurate state of health estimation, which is affected by different factors. According to the increase in electric and hybrid vehicles’ application, it is crucial to have a more accurate and reliable estimation of state of charge (SOC) and state of health (SOH) in different environmental conditions. This allows improving battery management system operation for optimal utilization of a battery pack in various operating conditions. This article proposes an approach to estimate battery capacity based on two parameters. First, a practical and straightforward method is introduced to assess the battery’s internal resistance, which is directly related to the battery’s remaining useful life. Second, the different least square algorithm is explored. Finally, a promising, practical, simple, accurate, and reliable technique is proposed to estimate battery capacity appropriately. The root mean square percentage error and the mean absolute percentage error of the proposed methods were calculated and were less than 0.02%. It was concluded the geometry method has all the advantages of a recursive manner, including a fading memory, a close form of a solution, and being applicable in embedded systems.

A constant and homogenous temperature control of Li-ion batteries is essential for a good performance, a safe operation, and a low aging rate. Especially when operating a battery with high loads in dense battery systems, a cooling system is required to keep the cell in a controlled temperature range. Therefore, an existing battery module is set up with a water-based liquid cooling system with aluminum cooling plates. A finite-element simulation is used to optimize the design and arrangement of the cooling plates regarding power consumption, cooling efficiency, and temperature homogeneity. The heat generation of an operating Li-ion battery is described by the lumped battery model, which is integrated into COMSOL Multiphysics. As the results show, a small set of non-destructively determined parameters of the lumped battery model is sufficient to estimate heat generation. The simulated temperature distribution within the battery pack confirmed adequate cooling and good temperature homogeneity as measured by an integrated temperature sensor array. Furthermore, the simulation reveals sufficient cooling of the batteries by using only one cooling plate per two pouch cells while continuously discharging at up to 3 C.

The powertrain in electric vehicles typically comprises various components, including lithium-ion batteries (LIBs), a battery management system, an energy converter, an electric motor, and a mechanical transmission system. Electric vehicles utilize the electrical energy stored in LIBs to efficiently drive the motors efficiently. LIBs find widespread use in portable electronic devices like laptops, mobile phones, and other electronic appliances, with potential applications in the automotive sector. To examine the thermal performance of LIBs across diverse applications and establish accurate thermal models for batteries, it is essential to understand heat generation. Numerous researchers have proposed various methods to determine the heat generation of LIBs through comprehensive experimental laboratory measurements. This study comprehensively explores diverse experimental and modeling techniques used to analyze the thermal behavior and heat generation of LIBs.

NaMnPO4 and NaFePO4, polyanion cathode materials, exist in two different phases maricite/natrophilite and maricite/olivine, respectively. Both natrophilite NaMnPO4 and olivine NaFePO4 are electrochemically active and possess a one-dimensional tunnel for sodium-ion migration; however, these two phases are thermodynamically unstable. Therefore, they can be synthesized through an electrochemical route. On the contrary, maricite (m)-NaMnPO4 and maricite (m)-NaFePO4 are thermodynamically stable forms but have a huge activation energy of their diffusion pathways for sodium extraction and insertion in the crystal structure, which hinders electrochemical reactions. Therefore, the electrochemical behaviour of commercial m-NaMnPO4 and m-NaFePO4 has been studied to find a way for enabling them electrochemically. Ball milling and thermal/mechanical carbon coating are employed to reduce the particle size to enhance the electrochemical performance and shorten the diffusion pathway. Moreover, ball milling leads to defects and partial phase transformation. The electrochemical performance of milled-coated NaMnPO4 and NaFePO4 has been thoroughly investigated and compared. The phase transition of NaFePO4 is revealed by a differential scanning calorimeter. As a result, the achievable capacities of both cathode materials are significantly enhanced up to ∼50 mAh.g−1 via the particle size reduction as well as by carbon coating. However, the side reactions and agglomeration problems in such materials need to be minimized and must be considered to enable them for applications.

Significant progress has recently been made in transportation automation to alleviate human faults in traffic flow. Recent breakthroughs in artificial intelligence have provided justification for replacing human drivers with digital control systems. This paper proposes the design of a self-adaptive real-time cruise control system to enable path-following control of autonomous ground vehicles so that a self-driving car can drive along a road while following a lead vehicle. To achieve the cooperative objectives, we use a multi-agent deep reinforcement learning (MADRL) technique, including one agent to control the acceleration and another agent to operate the steering control. Since the steering of an autonomous automobile could be adjusted by a stepper motor, a well-known DQN agent is considered to provide the discrete angle values for the closed-loop lateral control. We performed a simulation-based analysis to evaluate the efficacy of the proposed MADRL path following control for autonomous vehicles (AVs). Moreover, we carried out a thorough comparison with two state-of-the-art controllers to examine the accuracy and effectiveness of our proposed control system.